Whistlerlib: a distributed computing library for exploratory data analysis on large social network datasets.
Saved in:
| Title: | Whistlerlib: a distributed computing library for exploratory data analysis on large social network datasets. |
|---|---|
| Authors: | Garcia-Robledo, Alberto1 (AUTHOR), Espejel-Trujillo, Angelina1 (AUTHOR) aespejel@centrogeo.edu.mx |
| Source: | Multimedia Tools & Applications. Nov2024, Vol. 83 Issue 39, p87071-87104. 34p. |
| Subjects: | Social network analysis, Computer workstation clusters, Social informatics, Distributed computing, Distributed algorithms |
| Abstract: | At least 350k posts are published on X, 510k comments are posted on Facebook, and 66k pictures and videos are shared on Instagram each minute. These large datasets require substantial processing power, even if only a percentage is collected for analysis and research. To face this challenge, data scientists can now use computer clusters deployed on various IaaS and PaaS services in the cloud. However, scientists still have to master the design of distributed algorithms and be familiar with using distributed computing programming frameworks. It is thus essential to generate tools that provide analysis methods to leverage the advantages of computer clusters for processing large amounts of social network text. This paper presents Whistlerlib, a new Python library for conducting exploratory analysis on large text datasets on social networks. Whistlerlib implements distributed versions of various social media, sentiment, and social network analysis methods that can run atop computer clusters. We experimentally demonstrate the scalability of the various Whistlerlib distributed methods when deployed on a public cloud platform. We also present a practical example of the analysis of posts on the social network X about the Mexico City subway to showcase the features of Whistlerlib in scenarios where social network analysis tools are needed to address issues with a social dimension. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
